Automatic Segmentation of Brain Structures Based on Anatomic Atlas

The non-invasive in vivo nature of magnetic resonance imaging (MRI) makes it the modality of choice of many neuroanatomical imaging studies. This paper discusses automatic brain structure segmentation based on anatomic atlas. Our goal is to use image-processing algorithms and previous knowledge statistical models for segmentation and labeling of brain regions in order to support radiologists to make clinical diagnosis. Practical experiments show the results of brain tissue classification process and automatic region labeling in order to segment accurately the hippocampus and measure its volume. Hippocampus volumetric information can be useful to evaluate patients with Alzheimer´s disease. The final goal of this work is computer-aided diagnosis for brain diseases.